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Simulation-based Decision Support Framework for Hybrid Flow Shop (HFS) Scheduling Problem
하이브리드 플로우 샵 (HFS) 스케줄링 문제의 시뮬레이션 기반 의사 결정 지원 프레임 워크에 관한 연구

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dc.contributor.advisorPark, Jin Woo-
dc.contributor.authorSudipta, Saha-
dc.date.accessioned2017-10-31T07:35:22Z-
dc.date.available2017-10-31T07:35:22Z-
dc.date.issued2017-08-
dc.identifier.other000000145246-
dc.identifier.urihttps://hdl.handle.net/10371/137370-
dc.description학위논문 (석사)-- 서울대학교 대학원 공과대학 산업공학과, 2017. 8. Park, Jin Woo.-
dc.description.abstractManufacturing environments has become very complicated nowadays. They consist of hundreds of job varieties, diverse types of machines with complex architectural layouts. Hybrid flow shop (HFS) is one of them. Although there is no exact definition of HFS but flow shops with multiple parallel machines at each stage are referred as HFS in general. However, the characteristics of a hybrid flow shop might differ according to the particular production environment. HFS production scheduling is one of the most complex combinatorial problems encountered in many real world industries. Given HFSs complexity and importance, most of the literatures on HFS scheduling seem to focus on mono-criteria objectives which is sometimes quite unrealistic. Real world HFS scheduling problem involves several performance measures as objective functions, which eventually can often conflict and compete for decision making.
Industries have been using simulation extensively to model and analyze the impact of such variabilities on production system behavior and to explore several ways of coping under any changes or uncertainties. Simulation flexibility may help to find better or optimal solutions to a number of complex problems of HFS. The HFS scheduling problem requires all activities to be considered. Even though simulation is a good tool, there is one more aspect to be considered on using simulation. Almost each and every level of employees needs to be skilled enough with simulation software to deal with HFS scheduling problems. But not all of them are fully capable to utilize the simulation system. Inadequate capability of personnel to utilize simulation effectively can only be overcome if we can design custom interfaces and integrate flexible simulation framework with supportive programs.
In this study, a flexible Simulation modeling framework is proposed to mimic HFS systems. This research analyzes the impact of different combinations of commonly used job sequencing and dispatching policies for multiple performance measures. A heuristic is also proposed to reduce the number of comparisons thus to reduce the number of simulation runs. By implementing the proposed heuristics, better combinations of dispatching policies are found each of the performance measure considered. In the end, an analysis is shown regarding the impact of varying batch size on certain HFSs performance measures.
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dc.description.tableofcontentsChapter 1. Introduction ................................ .......................... 1
1.1 Background ................................ ................................ ............... 1
1.2 Motivation ................................ ................................ ................. 3
1.3 Outline of the thesis ................................ ................................ .. 4
Chapter 2. Literature Review ................................ ................. 5
2.1 Problems in HFS ................................ ................................ ....... 5
2.2 HFS scheduling problem (HFSP) ................................ .............. 6
2.2.1 HFS classifications .................................................................. 6
2.2.2 Performance criteria .............................................................. 10
2.3 Solution methods for HFSP ................................ .................... 13
2.3.1 Dispatching rules ................................................................... 15
2.3.2 Simulation ............................................................................. 15
Chapter 3. Problem Description ................................ ........... 17
3.1 Notations of parameters and variables ................................ .... 18
3.2 Objectives ................................ ................................ ................ 20
3.3 Constraints ................................ ................................ ............... 21
Chapter 4. Methodology ................................ ....................... 22
4.1 Simulation framework ................................ ............................. 22
4.2 Proposed dispatching policies ................................ ................. 24
4.2.1 Mathematical measures of dispatching policies .................... 25
4.3 Proposed heuristic ................................ ................................ ... 26
4.3.1 IWMF heuristic ..................................................................... 27
Chapter 5. Case Study ................................ .......................... 30
5.1 Introduction ................................ ................................ ............. 30
5.2 Optical lens processing system ................................ ............... 31
5.3 Design of Experiment ................................ ........................... 32
5.3.1 Analyzed job types ................................................................ 32
5.3.2 Layout of the analyzed HFS .................................................. 33
5.3.3 Simulation model setup ......................................................... 36
5.3.4 Assumptions for experiment ................................................. 37
5.3.5 Attributes for simulation experiment .................................... 38
5.4 Model development ................................ ................................ . 42
5.4.1 Job orders creation and routing ............................................. 42
5.4.2 Workstation design ................................................................ 42
5.4.3 Queue modules ...................................................................... 42
5.4.4 Interface control objects ........................................................ 43
5.4.5 Integration of dispatching rules ............................................. 43
Chapter 6. Results and Analysis ................................ ........... 44
6.1 Experiment criteria ................................ ................................ .. 44
6.2 Experiment procedure ................................ ............................. 44
6.2.1 Observations .......................................................................... 53
6.3 Varying batch size effects ................................ ........................ 57
6.3.1 Observations .......................................................................... 58
Chapter 7. Conclusion ................................ .......................... 60
7.1 Contribution ................................ ................................ ............ 60
7.2 Limitations ................................ ................................ .............. 61
7.3 Future work ................................ ................................ ............. 62
Bibliography ................................ ................................ ......... 63
APPENDIX A ................................ ................................ ....... 69
APPENDIX B ................................ ................................ ....... 72
APPENDIX C ................................ ................................ ....... 76
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dc.formatapplication/pdf-
dc.format.extent3876213 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectHybrid flow shop-
dc.subjectSimulation-
dc.subjectispatching rules-
dc.subjectHeuristic-
dc.subjectVarying batch size-
dc.subject.ddc670.42-
dc.titleSimulation-based Decision Support Framework for Hybrid Flow Shop (HFS) Scheduling Problem-
dc.title.alternative하이브리드 플로우 샵 (HFS) 스케줄링 문제의 시뮬레이션 기반 의사 결정 지원 프레임 워크에 관한 연구-
dc.typeThesis-
dc.contributor.AlternativeAuthor수딥타-
dc.description.degreeMaster-
dc.contributor.affiliation공과대학 산업공학과-
dc.date.awarded2017-08-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Theses (Master's Degree_산업공학과)
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